Chronic Disease and Self-Injection: Ethnographic Investigations into the Patient Experience During Treatment
Notice bibliographique
Résumé
Drug administration by self-injection provides an option to treat chronic inflammatory diseases such as rheumatoid arthritis (RA) and Crohn’s disease (CD). However, a negative self-injection experience for patients may reduce patient adherence to the recommended treatment regimen. In this study, a holistic approach was used to identify common themes along the treatment pathway and at self-injection that, if changed, could improve patient experience and treatment outcomes. Two ethnographic studies were conducted: Field Insights CODE (FI[CODE]) examined the treatment pathway within the context of the experience of living with RA or CD, and Injection Mission 2020 (IM2020) focused on the moment of self-injection. FI(CODE) used an open ethnographic approach to interview 62 patients and 10 healthcare professionals (HCPs) from the US and UK. IM2020 included a review of over 50 injection device design information sources from the sponsor, and interviews with 9 patients, 8 HCPs, and 5 medical device designers from the US, UK, Canada, and Japan. FI(CODE) identified suboptimal treatment practices along the treatment pathway in four key areas: treatment team communication, treatment choice, patient empowerment, and treatment delivery. Patients with more treatment options and greater disease understanding were less likely to struggle with the treatment process. IM2020 demonstrated that five related components influenced the self-injection experience: delivery process, emotional state, social perception, educational level, and ritualization of the self-injection process. These analyses highlight several potential areas for improvement, including aligning the device more to patients’ needs to improve treatment adherence, better accessibility to educational resources to increase patient disease understanding, and guidance to empower patients to develop an optimal personalized self-injection ritual. UCB Pharma. Some medicines used to treat long-term conditions, such as rheumatoid arthritis or Crohn’s disease, are injected under the skin. Often, patients can choose to inject medicines themselves (self-injection). This must be done correctly for the medicines to work properly. But, the training surrounding self-injection is uneven and often cannot address the fundamental problems facing all self-injecting patients. What healthcare improvements could help patients self-inject successfully? To find out, we interviewed people living with rheumatoid arthritis or Crohn’s disease, while others were doctors, nurses, and people who design injection devices. We found four common problems in the overall healthcare that patients received: In addition, five factors were identified that affected patients’ experiences of self-injection: If doctors and nurses can support patients by providing a greater choice of treatments and injection devices, and teaching more about self-injection, this could improve patients’ experiences and allow medications to work better. Healthcare professionals should help patients to develop their own, optimal routine for self-injection.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».